Study of the Image Quality Assessment (IQA) focuses on how to use one or more objective indicators to objectively evaluate image quality. The objective evaluation may be an estimate of a subjective assessment of the image quality.
Image quality assessment may have broad applications. In the field of image de-noising, image restoration, image enhancement and image fusion, objective indicators of image quality assessment may be used to compare the performance of different algorithms or choose parameters for an algorithm. Additionally, in the field of image coding and communications, objective indicators of image quality assessment may be used to guide the image compression, transmission, reception, and evaluate the performance of different algorithms and systems.
Depending on how much information of a reference image is needed, in general, the objective image quality assessment algorithms may be divided into three types: Full-Reference (FR), Reduced-Reference (RR), and No-Reference (NR). However, there are numerous cases where a reference image may be unavailable. For instance, the assessment of the quality of a de-noising algorithm on an image, where the corresponding noise-free image is unknowable or unavailable. In this case, one may need to employ a “no-reference” or “blind” measure to render a quality assessment. A challenge that confronts conventional image quality assessment systems is to provide assessments when neither the reference image nor the image distortion type is known.
In the study of blind image quality assessment, a variety of perceptual features may be used to assess the image quality. Existing methods, after extracting features, are unable to determine the relationship between the features and image quality, and use training—test mode for image quality assessment. During the training stage, features extracted from training images together with their corresponding subjective scores may be used to train a regression model. During the test stage, the objective scores of test images corresponded to perceptual features extracted from the test images may be predicted using the trained regression model. Thus, there is the need for a system and method to robustly and efficiently assess image quality.